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ke our starting forecast. We viewed sales from each product to see if they fall under one the following situations: Base (Base + Trend) (Base + Trend) * Seasonality

When a product is base the sales alter little each sales period or change erratically with external market signals. An example of a product that would fall under the base model would be sand bags. Sand bags sell at the same level month after month. If a retailer sells a hundred bags in March the will sell a hundred bags in October. But, in a flood plain after terrantiel downpour, the sales of sandbags increase exponentially. This is because many people purchase the sandbags to hold back the rising flood waters. Another example of a product that would emulate the base model is insulin. There is a limited number of people with insulin dependant diabetes. The people with insulin dependant diabetes unfortunately die off, but are replaced with other people who fall ill to the disease. There is very little movement up or down in the sale of insulin.

The base plus trend model illustrates that a product has a trend of upward or downward groth in sales. Products at the begining or ending of their respective product cycles will display this type of performance. Sales of a new product such as Microsoft Windows95? disk operating system will fall into this category. The sales of May are expected to be larger than April, the sales of April will be larger than March and so on.

While the sales may decline (or increase) during a particular time frame, a trend of upward or downward growth will be apparent.

Lastly, the base plus trend times seasonality attempts to forecast the swings in demand that are caused by seasonal changes that can be expected to repeat themselves during a single or consecutive time period. For example, florists experience a predictable increases in demand each year, both occur at similiar (or exact) times during the year; Mothers Day and St. Valentines Day. Florists must forecast demand for roses and other flowers so they can meet this predictable demand. If I where to construct a ten year historical graph for a neighborhood florist, there would be clear increase in demand every February and May, in every one of those years. A caveat to the previous example would be that in most lines a business forecasting is never this easy. If it was there would not be a production management class or operations management science!

Some other methods used to forecast demand are: delphi method, historical analogy, simple moving average, box Jenkins type, exponential smoothing and regression analysis. Forecasting falls into four basic types: qualitative, time series analysis, casual relationships and simulation. All of the proceeding have pluses, minuses and degrees of accuraccy. I often depends on the precision of previous data. Also, as is often stated in financial prospectuses "past performance does not guarantee future results".

For product one I used base plus trend. The sales started of at 1246 units and gradually increased to 2146 at the end of twenty four months. There was a slight dip in sales between month nine-teen and month twenty three. This drop can from internal or external variables.

Product two was little more tricky. The swing where eratic and showed no detectable trend. I may have been able to use (Base + Trend) * Seasonality if there was not a decrease in sales from month eight and an increase in sales in本论文由英语论文网提供整理，提供论文代写，英语论文代写，代写论文，代写英语论文，代写留学生论文，代写英文论文，留学生论文代写相关核心关键词搜索。